"""simple docstring"""


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


a				=logging.getLogger(__name__)






@dataclass
class   __UpperCAmelCase		:
				A__ :  str
				A__ :  List[str]
				A__ :  Optional[List[str]]






@dataclass
class   __UpperCAmelCase		:
				A__ :  List[int]
				A__ :  List[int]
				A__ :  Optional[List[int]]    				=			None
				A__ :  Optional[List[int]]    				=			None






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  Optional[Any]    				=			'''train'''
				A__ :  List[str]    				=			'''dev'''
				A__ :  Optional[int]    				=			'''test'''






class   __UpperCAmelCase		:

				@staticmethod
				def 						_a      ( _lowerCamelCase      ,					_lowerCamelCase ):
										raise NotImplementedError

				@staticmethod
				def 						_a      ( _lowerCamelCase ):
										raise NotImplementedError






				@staticmethod
				def 						_a      ( _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=False      ,					_lowerCamelCase="[CLS]"      ,					_lowerCamelCase=1      ,					_lowerCamelCase="[SEP]"      ,					_lowerCamelCase=False      ,					_lowerCamelCase=False      ,					_lowerCamelCase=0      ,					_lowerCamelCase=0      ,					_lowerCamelCase=-100      ,					_lowerCamelCase=0      ,					_lowerCamelCase=True      ,					):
										lowerCamelCase__   ={label: i for i, label in enumerate(_lowerCamelCase )}

										lowerCamelCase__   =[]
										for ex_index, example in enumerate(_lowerCamelCase ):
																if ex_index % 10000 == 0:
																						logger.info("Writing example %d of %d"      ,					_lowerCamelCase      ,					len(_lowerCamelCase ) )

																lowerCamelCase__   =[]
																lowerCamelCase__   =[]
																for word, label in zip(example.words      ,					example.labels ):
																						lowerCamelCase__   =tokenizer.tokenize(_lowerCamelCase )

																						# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																						if len(_lowerCamelCase ) > 0:
																												tokens.extend(_lowerCamelCase )
																												# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																												label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_lowerCamelCase ) - 1) )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																lowerCamelCase__   =tokenizer.num_special_tokens_to_add()
																if len(_lowerCamelCase ) > max_seq_length - special_tokens_count:
																						lowerCamelCase__   =tokens[: (max_seq_length - special_tokens_count)]
																						lowerCamelCase__   =label_ids[: (max_seq_length - special_tokens_count)]

																# The convention in BERT is:
																# (a) For sequence pairs:
																#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
																#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
																# (b) For single sequences:
																#  tokens:   [CLS] the dog is hairy . [SEP]
																#  type_ids:   0   0   0   0  0     0   0
																#
																# Where "type_ids" are used to indicate whether this is the first
																# sequence or the second sequence. The embedding vectors for `type=0` and
																# `type=1` were learned during pre-training and are added to the wordpiece
																# embedding vector (and position vector). This is not *strictly* necessary
																# since the [SEP] token unambiguously separates the sequences, but it makes
																# it easier for the model to learn the concept of sequences.
																#
																# For classification tasks, the first vector (corresponding to [CLS]) is
																# used as the "sentence vector". Note that this only makes sense because
																# the entire model is fine-tuned.
																tokens += [sep_token]
																label_ids += [pad_token_label_id]
																if sep_token_extra:
																						# roberta uses an extra separator b/w pairs of sentences
																						tokens += [sep_token]
																						label_ids += [pad_token_label_id]
																lowerCamelCase__   =[sequence_a_segment_id] * len(_lowerCamelCase )

																if cls_token_at_end:
																						tokens += [cls_token]
																						label_ids += [pad_token_label_id]
																						segment_ids += [cls_token_segment_id]
																else:
																						lowerCamelCase__   =[cls_token] + tokens
																						lowerCamelCase__   =[pad_token_label_id] + label_ids
																						lowerCamelCase__   =[cls_token_segment_id] + segment_ids

																lowerCamelCase__   =tokenizer.convert_tokens_to_ids(_lowerCamelCase )

																# The mask has 1 for real tokens and 0 for padding tokens. Only real
																# tokens are attended to.
																lowerCamelCase__   =[1 if mask_padding_with_zero else 0] * len(_lowerCamelCase )

																# Zero-pad up to the sequence length.
																lowerCamelCase__   =max_seq_length - len(_lowerCamelCase )
																if pad_on_left:
																						lowerCamelCase__   =([pad_token] * padding_length) + input_ids
																						lowerCamelCase__   =([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																						lowerCamelCase__   =([pad_token_segment_id] * padding_length) + segment_ids
																						lowerCamelCase__   =([pad_token_label_id] * padding_length) + label_ids
																else:
																						input_ids += [pad_token] * padding_length
																						input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																						segment_ids += [pad_token_segment_id] * padding_length
																						label_ids += [pad_token_label_id] * padding_length

																assert len(_lowerCamelCase ) == max_seq_length
																assert len(_lowerCamelCase ) == max_seq_length
																assert len(_lowerCamelCase ) == max_seq_length
																assert len(_lowerCamelCase ) == max_seq_length

																if ex_index < 5:
																						logger.info("*** Example ***" )
																						logger.info("guid: %s"      ,					example.guid )
																						logger.info("tokens: %s"      ,					" ".join([str(_lowerCamelCase ) for x in tokens] ) )
																						logger.info("input_ids: %s"      ,					" ".join([str(_lowerCamelCase ) for x in input_ids] ) )
																						logger.info("input_mask: %s"      ,					" ".join([str(_lowerCamelCase ) for x in input_mask] ) )
																						logger.info("segment_ids: %s"      ,					" ".join([str(_lowerCamelCase ) for x in segment_ids] ) )
																						logger.info("label_ids: %s"      ,					" ".join([str(_lowerCamelCase ) for x in label_ids] ) )

																if "token_type_ids" not in tokenizer.model_input_names:
																						lowerCamelCase__   =None

																features.append(
																    InputFeatures(
																        input_ids=_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					token_type_ids=_lowerCamelCase      ,					label_ids=_lowerCamelCase ) )
										return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset






	class   __UpperCAmelCase		(      __lowerCAmelCase      ):
					A__ :  List[InputFeatures]
					A__ :  int    				=			nn.CrossEntropyLoss().ignore_index

					def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase = None      ,					_lowerCamelCase=False      ,					_lowerCamelCase = Split.train      ,					):
											# Load data features from cache or dataset file
											lowerCamelCase__   =os.path.join(
											    _lowerCamelCase      ,					"cached_{}_{}_{}".format(mode.value      ,					tokenizer.__class__.__name__      ,					str(_lowerCamelCase ) )      ,					)

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											lowerCamelCase__   =cached_features_file + ".lock"
											with FileLock(_lowerCamelCase ):
																	if os.path.exists(_lowerCamelCase ) and not overwrite_cache:
																							logger.info(F'''Loading features from cached file {cached_features_file}''' )
																							lowerCamelCase__   =torch.load(_lowerCamelCase )
																	else:
																							logger.info(F'''Creating features from dataset file at {data_dir}''' )
																							lowerCamelCase__   =token_classification_task.read_examples_from_file(_lowerCamelCase      ,					_lowerCamelCase )
																							# TODO clean up all this to leverage built-in features of tokenizers
																							lowerCamelCase__   =token_classification_task.convert_examples_to_features(
																							    _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					cls_token_at_end=bool(model_type in ["xlnet"] )      ,					cls_token=tokenizer.cls_token      ,					cls_token_segment_id=2 if model_type in ["xlnet"] else 0      ,					sep_token=tokenizer.sep_token      ,					sep_token_extra=_lowerCamelCase      ,					pad_on_left=bool(tokenizer.padding_side == "left" )      ,					pad_token=tokenizer.pad_token_id      ,					pad_token_segment_id=tokenizer.pad_token_type_id      ,					pad_token_label_id=self.pad_token_label_id      ,					)
																							logger.info(F'''Saving features into cached file {cached_features_file}''' )
																							torch.save(self.features      ,					_lowerCamelCase )

					def __len__( self ):
											return len(self.features )






					def __getitem__( self      ,					_lowerCamelCase ):
											return self.features[i]


if is_tf_available():
	import tensorflow as tf






	class   __UpperCAmelCase		:
					A__ :  List[InputFeatures]
					A__ :  int    				=			-1_0_0

					def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase = None      ,					_lowerCamelCase=False      ,					_lowerCamelCase = Split.train      ,					):
											lowerCamelCase__   =token_classification_task.read_examples_from_file(_lowerCamelCase      ,					_lowerCamelCase )
											# TODO clean up all this to leverage built-in features of tokenizers
											lowerCamelCase__   =token_classification_task.convert_examples_to_features(
											    _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					cls_token_at_end=bool(model_type in ["xlnet"] )      ,					cls_token=tokenizer.cls_token      ,					cls_token_segment_id=2 if model_type in ["xlnet"] else 0      ,					sep_token=tokenizer.sep_token      ,					sep_token_extra=_lowerCamelCase      ,					pad_on_left=bool(tokenizer.padding_side == "left" )      ,					pad_token=tokenizer.pad_token_id      ,					pad_token_segment_id=tokenizer.pad_token_type_id      ,					pad_token_label_id=self.pad_token_label_id      ,					)

											def gen():
																	for ex in self.features:
																							if ex.token_type_ids is None:
																													yield (
																													    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																													    ex.label_ids,
																													)
																							else:
																													yield (
																													    {
																													        "input_ids": ex.input_ids,
																													        "attention_mask": ex.attention_mask,
																													        "token_type_ids": ex.token_type_ids,
																													    },
																													    ex.label_ids,
																													)

											if "token_type_ids" not in tokenizer.model_input_names:
																	lowerCamelCase__   =tf.data.Dataset.from_generator(
																	    _lowerCamelCase      ,					({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)      ,					(
																	        {"input_ids": tf.TensorShape([None] ), "attention_mask": tf.TensorShape([None] )},
																	        tf.TensorShape([None] ),
																	    )      ,					)
											else:
																	lowerCamelCase__   =tf.data.Dataset.from_generator(
																	    _lowerCamelCase      ,					({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)      ,					(
																	        {
																	            "input_ids": tf.TensorShape([None] ),
																	            "attention_mask": tf.TensorShape([None] ),
																	            "token_type_ids": tf.TensorShape([None] ),
																	        },
																	        tf.TensorShape([None] ),
																	    )      ,					)

					def 						_a      ( self ):
											lowerCamelCase__   =self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )

											return self.dataset

					def __len__( self ):
											return len(self.features )






					def __getitem__( self      ,					_lowerCamelCase ):
											return self.features[i]



"""simple docstring"""


import random





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   dict:




						'''simple docstring'''



						lowerCamelCase__   ={i: [] for i in range(__lowerCAmelCase							)}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(__lowerCAmelCase							)
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(__lowerCAmelCase							):
												for j in range(i + 1  ,       __lowerCAmelCase							):
																		if random.random() < probability:
																								graph[i].append(__lowerCAmelCase							)
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(__lowerCAmelCase							)
						return graph





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   dict:




						'''simple docstring'''



						return {
						    i: [j for j in range(__lowerCAmelCase							) if i != j] for i in range(__lowerCAmelCase							)
						}


if __name__ == "__main__":
	import doctest

	doctest.testmod()


